CS: Compressed Counting, Data Stream Algorithms videos

This entry has a more Theoretical Computer Science (TCS) flavor to it:


Ping Li has released his latest paper on Compressed Counting. We mentioned a presentation on that subject and other work before.

Graham Cormode did a presentation/tutorial on Data stream algorithms last July. It was a tutorial presented at the Bristol Summer School on Probabilistic Techniques in Computer Science.

Many scenarios, such as network analysis, utility monitoring, and financial applications, generate massive streams of data. These streams consist of millions or billions of simple updates every hour, and must be processed to extract the information described in tiny pieces. These notes provide an introduction to (and set of references for) data stream algorithms, and some of the techniques that have been developed over recent years to help mine the data while avoiding drowning in these massive flows of information.

The video is here. The ppt slides are here while the pdf slides are here.

All the other videos of that meeting can be found here. It includes talks by:

• Ayalvadi Ganesh, on Random Graphs and Stochastic Processes
• Eyal Kushilevitz, on Communication Complexity.
• S. Muthu Muthukrishnan, on Auction Theory for Sponsored Search
• Joseph Naor, on Approximation Algorithms.
• Andreas Winter, on Concentration of Measure.
• Bella Bollobas, on Inhomogeneous Random Graphs

On a totally unrelated subject, my webcrawler only goes through publication pages. If you have an opening for a post that requires some understanding of compressive sensing and related matter, do not hesitate to let me know.



Credit: NASA/Johns Hopkins University Applied Physics Laboratory/Carnegie Institution of Washington, Mercury as never seen before. Image taken the day before yesterday (October 6th) by the Messenger spacecraft.